Interaction of an atypical Plasmodium falciparum ETRAMP with human apolipoproteins

Background: In order to establish a successful infection in the human host, the malaria parasite Plasmodium falciparum must establish interactions with a variety of human proteins on the surface of different cell types, as well as with proteins inside the host cells. To better understand this aspect of malaria pathogenesis, a study was conducted with the goal of identifying interactions between proteins of the parasite and those of its human host. Methods: A modified yeast two-hybrid methodology that preferentially selects protein fragments that can be expressed in yeast was used to conduct high-throughput screens with P. falciparum protein fragments against human liver and cerebellum libraries. The resulting dataset was analyzed to exclude interactions that are not likely to occur in the human host during infection. Results: An initial set of 2,200 interactions was curated to remove proteins that are unlikely to play a role in pathogenesis based on their annotation or localization, and proteins that behave promiscuously in the two-hybrid assay, resulting in a final dataset of 456 interactions. A cluster that implicates binding between P. falciparum PFE1590w/ETRAMP5, a putative parasitophorous vacuole membrane protein, and human apolipoproteins ApoA, ApoB and ApoE was selected for further analysis. Different isoforms of ApoE, which are associated with different outcomes of malaria infection, were shown to display differential interactions with PFE1590w. Conclusion: A dataset of interactions between proteins of P. falciparum and those of its human host was generated. The preferential interaction of the P. falciparum PFE1590w protein with the human ApoE e3 and ApoE e4 isoforms, but not the ApoE e2 isoform, supports the hypothesis that ApoE genotype affects risk of malaria infection. The dataset contains other interactions of potential relevance to disease that may identify possible vaccine candidates and drug targets.This work was supported in part by grant P50 GM64655 from the NIH

Huntingtin Interacting Proteins Are Genetic Modifiers of Neurodegeneration

Huntington's disease (HD) is a fatal neurodegenerative condition caused by expansion of the polyglutamine tract in the huntingtin (Htt) protein. Neuronal toxicity in HD is thought to be, at least in part, a consequence of protein interactions involving mutant Htt. We therefore hypothesized that genetic modifiers of HD neurodegeneration should be enriched among Htt protein interactors. To test this idea, we identified a comprehensive set of Htt interactors using two complementary approaches: high-throughput yeast two-hybrid screening and affinity pull down followed by mass spectrometry. This effort led to the identification of 234 high-confidence Htt-associated proteins, 104 of which were found with the yeast method and 130 with the pull downs. We then tested an arbitrary set of 60 genes encoding interacting proteins for their ability to behave as genetic modifiers of neurodegeneration in a Drosophila model of HD. This high-content validation assay showed that 27 of 60 orthologs tested were high-confidence genetic modifiers, as modification was observed with more than one allele. The 45% hit rate for genetic modifiers seen among the interactors is an order of magnitude higher than the 1%–4% typically observed in unbiased genetic screens. Genetic modifiers were similarly represented among proteins discovered using yeast two-hybrid and pull-down/mass spectrometry methods, supporting the notion that these complementary technologies are equally useful in identifying biologically relevant proteins. Interacting proteins confirmed as modifiers of the neurodegeneration phenotype represent a diverse array of biological functions, including synaptic transmission, cytoskeletal organization, signal transduction, and transcription. Among the modifiers were 17 loss-of-function suppressors of neurodegeneration, which can be considered potential targets for therapeutic intervention. Finally, we show that seven interacting proteins from among 11 tested were able to co-immunoprecipitate with full-length Htt from mouse brain. These studies demonstrate that high-throughput screening for protein interactions combined with genetic validation in a model organism is a powerful approach for identifying novel candidate modifiers of polyglutamine toxicity

A Human Protein Interaction Network Shows Conservation of Aging Processes between Human and Invertebrate Species

We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins

Endometriosis CNV association results at specific loci.

<p>Copy-Number-Variant (CNV data from a case∶control cohort was analyzed for association with endometriosis. Of 34 candidate loci identified using ParseCNV 22 loci passed a nominal significance threshold upon individual inspection and three of these passed the genome-wide significance threshold of 9.3×10⁻⁴. The coordinates reported are based on NCBI build 37, hg19 reference sequence.</p>a<p>p-Values were calculated using Fisher's exact test.</p>b<p>CNV is located 20,000 bp downstream of SGCZ.</p>c<p>Flanking genes over 90 kb away.</p>d<p>The analysis of the X chromosome included 1,845 endometriosis cases and 6,640 female population control subjects.</p

SNP association in CNV regions.

<p>The SNPs located within the eighteen autosomal CNV regions have previously been evaluated for association with endometriosis (Albertsen et al.), where they failed to pass the genome-wide significance threshold (p<5×10⁻⁸) applied in GWA studies. Using the less stringent criteria applied to candidate genes, we found that both regions have 7 non-LD SNPs (r²<0.2) which provide an adjusted significance threshold of 0.007. The two SNPs listed here have genotypic p-Values that are significant at this threshold. Both SNPs were in Hardy-Weinberg equilibrium (p>10⁻³).</p

Endometriosis Is Associated with Rare Copy Number Variants

<div><p>Endometriosis is a complex gynecological condition that affects 6–10% of women in their reproductive years and is defined by the presence of endometrial glands and stroma outside the uterus. Twin, family, and genome-wide association (GWA) studies have confirmed a genetic role, yet only a small part of the genetic risk can be explained by SNP variation. Copy number variants (CNVs) account for a greater portion of human genetic variation than SNPs and include more recent mutations of large effect. CNVs, likely to be prominent in conditions with decreased reproductive fitness, have not previously been examined as a genetic contributor to endometriosis. Here we employ a high-density genotyping microarray in a genome-wide survey of CNVs in a case-control population that includes 2,126 surgically confirmed endometriosis cases and 17,974 population controls of European ancestry. We apply stringent quality filters to reduce the false positive rate common to many CNV-detection algorithms from 77.7% to 7.3% without noticeable reduction in the true positive rate. We detected no differences in the CNV landscape between cases and controls on the global level which showed an average of 1.92 CNVs per individual with an average size of 142.3 kb. On the local level we identify 22 CNV-regions at the nominal significance threshold (P<0.05), which is greater than the 8.15 CNV-regions expected based on permutation analysis (P<0.001). Three CNV's passed a genome-wide P-value threshold of 9.3×10⁻⁴; a deletion at <i>SGCZ</i> on 8p22 (P = 7.3×10⁻⁴, OR = 8.5, Cl = 2.3–31.7), a deletion in <i>MALRD1</i> on 10p12.31 (P = 5.6×10⁻⁴, OR = 14.1, Cl = 2.7–90.9), and a deletion at 11q14.1 (P = 5.7×10⁻⁴, OR = 33.8, Cl = 3.3–1651). Two SNPs within the 22 CNVRs show significant genotypic association with endometriosis after adjusting for multiple testing; rs758316 in <i>DPP6</i> on 7q36.2 (P = 0.0045) and rs4837864 in <i>ASTN2</i> on 9q33.1 (P = 0.0002). Together, the CNV-loci are detected in 6.9% of affected women compared to 2.1% in the general population.</p></div

Post-filter CNV counts and relative CNV frequency.

<p>The CNV counts shown here represent the 43,560 candidate CNVs that remain after applying the Post-filter. The first column shows a specific CNV count. The second set of columns show the number of control and case individuals observed at each given CNV count. The center columns show the cumulative count of CNVs, and the last columns show the frequency at which a given number of CNVs are observed in each group of study-participants. A small subset of both case and control samples show highly inflated CNV counts with the highest CNV-counts being 41 and 231 in cases and controls respectively. A review of the individual CNVs in this group revealed that the vast majority of these CNVs are short (less than 20 SNPs), incorrectly called variants of the type CN = 1 and CN = 3. In fact, based on the visual inspection we generally found about 1–3 true CNVs per sample in this group. Using a systematic assessment to identify outliers we classified samples with more than 6 CNVs as outliers prone to increasingly high false-CNV counts.</p

Average CNV profiles in Cases and Controls with outliers removed.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103968#pone-0103968-t004" target="_blank">Table 4</a> shows the average CNV profiles in cases and controls after outlier removal. The probe count is specific to the Illumina Omniexpress platform and dependent on the SNP-filters we applied, while the CNV count and lengths are likely to reflect true population averages for CNVs about 50 kb in length or larger.</p

Overall comparisons of autosomal CNVs observed across the case and control populations after filtering are shown in the panels above.

<p>The data represented here reflect samples with CNV-count ≥1 after outlier-removal (cases = 1,750, controls = 14,858), autosomal probes with call-rate ≥99% (n = 533,512), and filtered CNVs (n = 38,609). Panel A show the frequency of CNVs by probe-count in various bin-sizes (10–14 probes; 15–19 probes; etc.), and Panel B show observed CNV-lengths in various bin-sizes (25 kb–49 kb; 50 kb–99 kb; etc.). The combined length of CNVs observed per individual is shown in Panel C. The case and control distributions in each panel are statistically similar implying that on a global level there is no difference between cases and controls in this study.</p

Filtered CNV counts stratified by copy-number state before and after outlier removal.

<p>The table summarizes the filtered CNV counts by copy-number state before and after outlier removal. A group of 77 cases (3.6%) and 351 controls (2.0%) percentage of samples were found to have very high CNV-counts (>6). Visual inspection of many of these CNVs revealed that a majority of these CNVs are false positives and that these samples generally have 1–3 true CNVs. Based on this observation we applied an outlier-removal filter to minimize the inflation of CNV-counts caused by sample specific and systematic effects. The frequency of each CN state is shown in parenthesis. After outlier removal the frequency of the different CN-states are quite similar in the case and control populations.</p